Personalized Recommendation for Mobile Internet Wealth Management Based on User Behavior Data Analysis

Abstract

Economic development has provided good opportunities for the development of securities companies. Similarly, the development of Internet technology has also brought huge opportunities and challenges to the development of securities companies. Aiming at the current wealth management issues in the era of mobile Internet, this article attempts to develop a personalized recommendation approach on the basis of users’ behavioral data analysis. We analyzed and judged the current situation of mobile Internet wealth management using personalized recommendation systems. On the basis of personalized recommendation, we use the user’s interest tags, personalized recommendation technology, and data mining technology to analyze and summarize customer transaction records. This is done through the use of preservation of customer transaction data. By understanding customers’ investment needs, risk preferences, and other information, we can segment customers and provide them with targeted products and services. As a result of the study, a flexible personalized recommendation framework is designed and validated for mobile Internet wealth management services. The effectiveness of the proposed approach is verified through testing of the developed model.